Social networking platforms have become an increasingly popular medium for consumers to share their opinions on products and/or services. Social network services allow users to easily connect with friends, family members, and the public to share, among other things, satisfaction or dissatisfaction with current products and services, wish lists for upcoming product features and services, comparisons between product and service offerings, and the like. As social networking has continued to grow, companies have recognized value in the technology. For instance, companies have found that social networking provides a great tool for gathering marketing research data. While many companies can create their own social networking profiles for communicating with consumers via social networking posts and other messages, these such companies can also mine social data on social media platforms and forums all around the world wide web to identify what consumers are saying about the company, its products, services, and industry in general.
In order for companies to find social networking posts relevant to their search, a rather complex query must be constructed to extract posts of relevance while filtering out irrelevant “noise.” The unstructured nature of social networking data, however, introduces a number of challenges for these companies when constructing queries, particularly because traditional search terms are generally insufficient for filtering out the noise. For instance, the 140 character limit on Twitter often encourages social media users to use grammatically incorrect or informal language. In order to appropriately construct a query capable of listening to relevant content, while taking into account the regularly varying informalities, companies must laboriously construct extremely large queries capable of extracting social networking content which may still fall short of desired relevance.